Edge-computing-based bottom-up hierarchical architecture for data administration in a digital network

ABSTRACT

A bottom-up hierarchical computer network architecture is provided. The architecture may include a central server. The architecture may also include a plurality of edge nodes that may be coupled to the central server. At least a first one of the edge nodes may be configured to process a transaction, compile data associated with the transaction, and store the data as a master dataset in the first edge node. The architecture may also include a data administration module. The data administration module may be configured to compare the master dataset in the first edge node to transactional data in the central server. When the transactional data in the central server is inconsistent with the master dataset in the first edge node, the data administration module may be configured to update the transactional data in the central server to be consistent with the master dataset in the first edge node.

FIELD OF TECHNOLOGY

Aspects of the disclosure relate to digital systems. Specifically,aspects of the disclosure relate to architectures for dataadministration in digital systems.

BACKGROUND OF THE DISCLOSURE

Many entities operate digital networks. Digital networks may includemultiple node devices. Each node device may receive, generate, and/ortransmit data. The data may be associated with the network.

The large amount of data accumulated (e.g., via the nodes) in thenetwork can be taxing to the resources of the network. The resources mayinclude memory, processing power, communication bandwidth, energy, andother suitable network resources.

Furthermore, accumulating data from disparate elements in the network(e.g., nodes, central servers, etc.) creates an environment that isvulnerable to duplicates and inconsistencies within the data.

It would be desirable, therefore, to provide systems and methods fordata administration in digital networks. It would be further desirable,for the systems and methods to reduce resource utilization, and promotedata consistency, across the network.

SUMMARY OF THE DISCLOSURE

Aspects of the disclosure relate to systems and methods for bottom-uphierarchical data administration in a computerized network. Thecomputerized network may include a central server. The central servermay be coupled to a plurality of edge nodes.

Methods may include compiling, via a first one of the edge nodes, dataassociated with a transaction. The methods may also include storing thedata as a master dataset in a memory of the first edge node. The methodsmay further include comparing the master dataset to transactional datastored in the central server.

When the transactional data in the central server is inconsistent withthe master dataset in the first edge node, the methods may includeupdating the transactional data in the central server to be consistentwith the master dataset in the first edge node.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the disclosure will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative network architecture in accordance withprinciples of the disclosure;

FIG. 2 shows another illustrative network architecture in accordancewith principles of the disclosure;

FIG. 3 shows an illustrative bottom-up hierarchical architecture inaccordance with principles of the disclosure; and

FIG. 4 shows an illustrative flowchart in accordance with principles ofthe disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Aspects of the disclosure relate to systems and methods foredge-computing-based bottom-up hierarchical architectures for dataadministration in a digital network. Edge-computing as used hereinrefers to computing networks and environments that utilize elements, ornodes, at the logical edge of the network. The nodes may also havesufficient computational power to process the data at the logical edgeof the network. The logical edge of a network may include portions ofthe network that are at, or close to, the interface with the environmentoutside of the network.

An edge-node may be a node on the periphery or edge of a network. Anillustrative network may be an internet-of-things (“IoT”) network. AnIoT network may include one or more nodes. Each node may include two ormore nodes.

A node may include, or may be, a sensor. A sensor may detect changes inattributes of a physical or virtual operating environment. For example,sensors may measure attributes such as audio, rainfall, movement,temperature, water levels, human activity, or activity of other sensors.Sensors may measure electronic network traffic, customer traffic,resource usage, electronic signals (e.g., input or output) or frequencyof user logins within a predefined geographic area.

Nodes may be any suitable size. For example, nodes may be a fewmillimeters in size. Nodes may be deployed in a wide variety oflocations. For example, sensors may be deployed in militarybattlefields, industrial plants, in orchards, in clothing, automobiles,smartphones, jewelry, refrigerators, institutions, or any other suitablelocation. Sensors may be relatively inexpensive and have low energyconsumption. Sensors may “sense” two or more stimuli or environmentalattributes.

Nodes may implement two or more functions. For example, sensors maymeasure changes in their operating (physical or virtual) environment,capture data corresponding to the measured changes and store/communicatethe captured data. Sensors may be accessed by other sensors or othernodes on the network.

A node may include, or may be, an actuator. For example, based on datacaptured by a sensor, an actuator may respond to a detected event. Basedon the capture and analysis of multiple sources of data (e.g., capturedby sensors), an actuator may be instructed to take action autonomously,without human intervention.

Actuators may respond to data transmitted or processed by other nodes.Actuators may include devices that modify the physical state of aphysical entity. Actuators may include devices that modify a virtualstate of information. Actuators may move (translate, rotate, etc.)physical objects or activate/deactivate functionalities of physicalobjects.

For example, actuators may dim a light bulb, open a door, change atemperature setting, authorize access to an ATM and/or any othersuitable functionality. Actuators may push notifications or redistributeresources. For example, notifications may route resources consumers(e.g., customers) to a location that has available resources to servicethe consumption.

Within an IoT environment, sensor nodes may perform the functions ofinput devices—they serve as “eyes” collecting information about theirnative operating environment. In contrast, actuator nodes may act as“hands” implementing decisions based on data captured by the sensornodes. A single node may include the functions of sensors and actuators.

Nodes may include an application programming interface (“API”) forcommunicating with other nodes. Nodes may communicate directly withother nodes using machine-to-machine (“M2M”) protocols. Illustrative M2Mprotocols may include MQ Telemetry Transport (“MQTT”). M2M includescommunication between two or more objects without requiring direct humanintervention. M2M communications may automate decision-making andcommunication processes for actuators.

Nodes may store captured data locally. For example, nodes may storecaptured data in on-board transitory and/or non-transitory computerreadable media. A node may transmit data. Data captured by a node may betransmitted to another node. A node may transmit data to a network core.

The network core may process the data. For example, multiple sensors maytransmit captured data to a cloud computing environment. The cloudcomputing environment may itself include multiple nodes, such ascomputer servers or other computer systems. Nodes of the cloud computingenvironment may be networked to each other.

The cloud computing environment may process data that was captured byother nodes far from the location where the data was generated. Forexample, captured data may be transmitted from one node to another nodeuntil the captured data reaches a centrally located data repository.

Data captured by nodes in an operating environment may be voluminous andcomplex (e.g., structured/unstructured and/or constantly changing).Traditional data processing application software may be inadequate tomeaningfully process the voluminous and complex data (e.g., “big data”).A cloud computing environment may include software applicationsspecially designed to process large volumes of data (“big dataanalytics”).

Nodes may communicate with other nodes directly, without transmittinginformation to an intermediary node or central server, such as a cloudcomputing environment. Data may be transmitted by a node using anysuitable transmission method. For example, data captured by a node maybe transmitted from a smartphone via a cellular network. Nodes mayleverage a communication link provided by a smartphone to communicatecaptured data to other nodes.

As a result of the disparate nature of nodes, a networked operatingenvironment may support a variety of communication protocols.Illustrative supported protocols may include HyperText Transfer Protocol(“HTTP”), Simple Object Access Protocol (“SOAP”), REpresentational StateTransfer (“REST”) Constrained Application Protocol (“CoAP”), SensorML,Institute of Electrical and Electronic Engineers (“IEEE”) 802.15.4(“ZigBee”) based protocols, IEEE 802.11 based protocols. For example,ZigBee is particularly useful for low-power transmission and requiresapproximately 20 to 60 milli-watts (“mW”) of power to provide 1 mWtransmission power over a range of 10 to 100 meters and a datatransmission rate of 250 kilo-bits/second.

To further conserve energy, a node may communicate wirelessly for shortperiods of time. Utilizing this approach, one or more standard sizesingle cell dry battery batteries (e.g., AA size) may provide a nodewith requisite computing power and wireless communication for manymonths.

Communication protocols used by nodes may not have, or may not becapable of having, security capabilities. A security layer or buffer maybe implemented by nodes that receive or rely on data captured byunsecured sensors. Nodes may be dynamically connected or disconnectedfrom a group or consortium. A security layer or buffer may be modularand scalable meet network node growth/contraction requirements.

A physical layer may link nodes within a network. The physical layer mayprovide data ports and communication pathways to move data betweenmultiple sub-networks and nodes. Such communication pathways may bewired or wireless. Exemplary wireless communication pathways may includeEthernet, Bluetooth, Wi-Fi, 3G, 4G, 5G and any other suitable wired orwireless broadband standards. Illustrative data ports of nodes mayinclude hardware and/or software for receiving and/or transmitting datausing any suitable communication pathway.

Each node may be assigned a unique identifier. For example, nodes may beidentified by one or more radio frequency identification (“RFID”) tags.The RFID tag may be stimulated to transmit identity information aboutthe node or any other information stored on the RFID tag. Nodes may beidentified by an Internet Protocol (“IP”) address. Nodes may beidentified based on a user. For example, a smartphone may be a nodeidentified based on a user that successfully inputs biometriccredentials.

Nodes may be positioned in, and capture data from, diverse operatingenvironments. Operating environments may include geographic locations orvirtual locations on electronic networks. Captured data may betransmitted to a location where information is needed for decisioning orconsumption. Such a location may not be the same location where the datawas captured or generated. Data synchronization protocols and cachingtechniques may be deployed across an IoT network to facilitatetransmission of data, or delivery of data to, any desired node.

For example, a location where data is captured may not have continuous,reliable network connectivity. Accordingly, captured data may be storedlocally on a node until a network connection is available to transmit orbroadcast the captured data to another node.

Nodes may be grouped. Nodes may be grouped based on physical proximityor based on the content (or expected content) of data captured by thesensor. Nodes may be grouped based on detected movement of a node. Forexample, nodes may be affixed to vehicles or other moveable objects.Such nodes may move in or out of a network. Nodes within a geographicarea may be grouped based on their presence within the geographic area.For example, nodes in and/or around a room, building, or institution, ora portion thereof, may form a group. Nodes may be grouped based on theirexpected trajectory. Nodes may be grouped based on whether they areresource consumer or providers. Nodes may be grouped based on expectedresource consumption. Nodes may be grouped virtually. Grouped nodes mayform a sub-network.

Contextually, data captured by nodes may provide information not onlyabout the native (physical or virtual) operating environment surroundinga node, but data captured by multiple nodes may provide data thatsignifies occurrence an event. The data may be analyzed by a cloudcomputing environment. Analytical tools (e.g., big data analysistechniques) may detect, within the data, occurrence of an event thattriggers actuator nodes to take responsive action.

Advances in embedded systems, such as System-on-a-Chip (SoC)architectures, have fueled development of nodes that are powerful enoughthemselves to run operating systems and complex data analysisalgorithms. An illustrative SoC may include a central processing unit(“CPU”), a graphics processor (“GPU”), memory, power managementcircuits, and communication circuit. Within an operating environment,such nodes may be positioned closer (relative to the cloud computingenvironment) to other data gathering nodes such as sensors. Nodespositioned close to the source of generated data and having sufficientcomputational power to process the data may be termed “edge-nodes”(alternatively, “edge nodes”). Edge-nodes may integrate sensingcapabilities, actuating capabilities, data connectivity and/or computingcapacities.

Edge-nodes may control sensors, actuators, embedded devices and othernodes. Edge-nodes, or the nodes they control, may not be continuouslyconnected to a network. Edge-nodes may provide computational resourcespositioned near the source of captured data or near an operatingenvironment. Processing data using edge-nodes may reduce thecommunication bandwidth needed to transmit data from a node to a cloudcomputing environment.

For example, a sensor deployed in a windfarm turbine may detect changesin wind speed or wind direction. Typically, the sensor may transmit thedetected changes to a remote cloud computing environment. The remotecloud computing environment may process data received from the node (andother nodes) and issue instructions to adjust a position of the turbinein response to the detected changes. However, communication with, andprocessing by, the cloud computing environment may inject additionallatency before the turbines are adjusted in response to the sensedchanges.

By running data analytics and processing closer to the originatingsource of data, actuator response times may be improved. Edge-nodesembedded in the turbine may include sufficient processing power toanalyze sensed data and adjust turbines with less latency (perhaps evenin close to real-time) and thereby optimize electricity production ofthe turbine.

In addition to providing faster response time to sensed changes,processing data using edge-nodes may reduce communication bandwidthrequirements and improve overall data transfer time across a network.Furthermore, less frequent data transmissions may enhance security ofdata gathered by nodes. Frequent data transfers may expose more data tomore potential security threats. For example, transmitted data may bevulnerable to being intercepted en-route to the cloud computingenvironment.

Additionally, edge-nodes may be tasked with decision-makingcapabilities. Edge-nodes may discard non-essential data generated bysensors. Such disregarded data may never be transmitted or stored in thecloud computing environment, further reducing exposure of such data tosecurity threats.

For example, a network of security cameras (e.g., sensor nodes) maygenerate large amounts of video data. Transmitting such large amounts ofdata to a cloud computing environment may utilize significantbandwidth—possibly preventing the cloud computing environment fromtimely receiving other data. Edge-nodes may analyze the video data atthe source, before transmitting the data to the cloud computingenvironment. The analysis by the edge-nodes may identify “important”video footage and discard the rest. Only the important video footage maybe transmitted to the cloud computing environment, reducing networkcongestion.

Often, instructions to actuators need to be issued in milliseconds orfaster. Round-trip communication to a cloud computing environmentintroduces undesirable latency. For some applications, necessaryreliability and critical-path control management make it undesirable towait for the cloud computing environment to process data and issueresponsive instructions.

For example, an anti-collision algorithm for an autonomous vehicle maybe executed by the cloud computing environment. However, it would befaster and more reliable for such anti-collision algorithms to be run byedge-nodes. Furthermore, the anti-collision data may have short-termvalue and it would therefore be undesirable to regularly transmit thatdata to the cloud computing environment.

Some nodes may be deployed in areas with poor network connectivity. Forexample, industries such as mining, oil/gas, chemicals and shipping maynot be well served by robust communication infrastructure. Incorporatingedge-nodes may allow networks associated with these industries toprocess data without robust communication infrastructure.

Smartphones may not have access to a data connection. Edge-nodes mayallow a cached version of a website to be opened on a smartphone,without an internet connection. Data may be entered into the website andchanges saved locally to the edge-node (e.g., the smartphone itself).The edge-node may sync changes with the cloud computing environment whena data connection is available. Aggregated sensor data may betransmitted to the cloud computing environment at designated times, suchas when network bandwidth is underutilized.

Utilizing edge-nodes to process data may improve security of a network.For example, a network breach may be detected by an edge-node. Theintrusion may be quarantined by or at the edge-node and prevent thebreach from compromising the entire network.

Edge-nodes may run encryption algorithms and store biometric informationlocally. Such dispersion of security protocols may reduce the risk ofany user's security information being comprised. Utilizing edge-nodesmay disperse processing power needed to run the security or encryptionalgorithms.

Utilizing edge-nodes may improve reliability of a network. For example,edge-nodes with machine learning capabilities may detect operationaldegradation in nodes, equipment, and infrastructure deployed within anoperating environment. Early detected degradation may be cured beforedeveloping into full-blown failures.

Generally, edge-nodes may include a processor circuit. The processorcircuit may control overall operation of an edge-node and its associatedcomponents. A processor circuit may include hardware, such as one ormore integrated circuits that form a chipset. The hardware may includedigital or analog logic circuitry configured to perform any suitable(e.g., logical) computing operation.

An edge-node may include one or more of the following components: I/Ocircuitry, which may include a transmitter device and a receiver deviceand may interface with fiber optic cable, coaxial cable, telephonelines, wireless devices, PHY layer hardware, a keypad/display controldevice or any other suitable encoded media or devices; peripheraldevices, which may include counter timers, real-time timers, power-onreset generators or any other suitable peripheral devices; a logicalprocessing device, which may compute data structural information,structural parameters of the data, quantify indices; andmachine-readable memory.

Machine-readable memory may be configured to store, in machine-readabledata structures: captured data, computer executable instructions,electronic signatures of biometric features or any other suitableinformation or data structures. Components of a node may be linked by asystem bus, wirelessly or by other suitable interconnections. Edge-nodecomponents may be present on one or more circuit boards. In someembodiments, the components may be integrated into a single chip. Thechip may be silicon-based.

The node may include RAM, ROM, an input/output (“I/O”) module and anon-transitory or non-volatile memory. The I/O module may include amicrophone, button and/or touch screen which may accept user-providedinput. The I/O module may include one or more of a speaker for providingaudio output and a video display for providing textual, audiovisualand/or graphical output.

Software applications may be stored within the non-transitory memoryand/or other storage medium. Software applications may provideinstructions to the processor that enable an edge-node to performvarious functions. For example, the non-transitory memory may storesoftware applications used by an edge-node, such as an operating system,application programs, and an associated database. Alternatively, some orall of computer executable instructions of an edge-node may be embodiedin hardware or firmware components of the edge-node.

Software application programs, which may be used by an edge-node, mayinclude computer executable instructions for invoking user functionalityrelated to communication, such as email, short message service (“SMS”),and voice input and speech recognition applications. Softwareapplication programs may utilize one or more algorithms that requestalerts, process received executable instructions, perform powermanagement routines or other suitable tasks.

An edge-node may support establishing network connections to one or moreremote nodes. Such remote nodes may be edge-nodes, sensors, actuators orother computing devices. Edge-nodes may be personal computers orservers. An edge-node may communicate with other nodes using a dataport. The data port may include a network interface or adapter. The dataport may include a communication circuit. An edge-node may include amodem, antenna or other communication circuitry for establishingcommunications over a network, such as the Internet. The communicationcircuit may include the network interface or adapter. The communicationcircuit may include the modem.

Via the data port and associated communication circuitry, an edge-nodemay access network connections and communication pathways external tothe edge-node. Illustrative network connections may include a local areanetwork (“LAN”) and a wide area network (“WAN”), and may also includeother networks. Illustrative communication pathways may include Wi-Fi,wired connections, Bluetooth, cellular networks, satellite links, radiowaves, fiber optic or any other suitable medium for carrying signals.

The existence of any of various well-known protocols such as TCP/IP,Ethernet, FTP, HTTP and the like is presumed, and a node can be operatedin a client-server configuration to permit a user to retrieve web pagesfrom a web-based server. Web browsers can be used to display andmanipulate data on web pages.

Edge-nodes may include various other components, such as a display,battery, speaker, and antennas. Edge-nodes may be portable devices suchas a laptop, tablet, smartphone, other “smart” devices (e.g., watches,eyeglasses, clothing having embedded electronic circuitry) or any othersuitable device for receiving, storing, transmitting and/or displayingelectronic information.

An edge-node may include a display constructed using organic lightemitting diode (“OLED”) technology. OLED technology may enhancefunctionality of an edge-node. OLEDs are typically solid-statesemiconductors constructed from a thin film of organic material. OLEDsemit light when electricity is applied across the thin film of organicmaterial. Because OLEDs are constructed using organic materials, OLEDsmay be safely disposed without excessive harm to the environment.

Furthermore, OLEDs may be used to construct a display that consumes lesspower compared to other display technologies. For example, in a LiquidCrystal Display, power must be supplied to the entire backlight, even toilluminate one pixel in the display. In contrast, an OLED display doesnot necessarily include a backlight. Furthermore, in an OLED display,preferably, only the illuminated pixel draws power.

The power efficiency of OLED technology presents a possibility fordesigning edge-nodes that consume less power for their basicfunctionality and allow any residual available power to provide enhancedsecurity and functionality. Illustrative devices that may be constructedusing OLED technology are disclosed in commonly assigned U.S. Pat. No.9,665,818, which is hereby incorporated by reference herein in itsentirety.

An edge-node may be, and may be operational with, numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with this disclosureinclude, but are not limited to, personal computers, server computers,handheld or laptop devices, tablets, “smart” devices (e.g., watches,eyeglasses, clothing having embedded electronic circuitry) mobile phonesand/or other personal digital assistants (“PDAs”), multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

Edge-nodes may utilize computer-executable instructions, such as programmodules, executed by a processor. Software applications may includemultiple program modules. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Anedge-node may be operational with distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices. Edge-nodes mayinteract with a network of remote servers hosted on the Internet tostore, manage, and process data (e.g., a cloud computing environment).

An edge-node may receive and/or transmit data in real-time or atpre-defined intervals, such as once a day. The edge-node may filter datacaptured by one or more nodes. The edge-node may repackage or reformatcaptured data.

Edge-nodes may include a battery. The battery may be a power source forelectronic components of the edge-node. For example, the battery maysupply power to the display, the communication circuit and the processorcircuit. In some embodiments, an edge-node may include a plurality ofbatteries. Edge-nodes may include solar panels that convert solar energyinto electricity that power one or more components of an edge-node.

A method for bottom-up hierarchical data administration in acomputerized network is provided. A traditional hierarchicalarchitecture may include an architecture with multiple tiers, andauthority may increase with the higher tiers. Often, the higher tiersalso contain fewer participants than the lower tiers. A bottom-upapproach may include architectures where the participants in the lowertiers influence the participants of the higher tiers. The disclosedsystems and methods may utilize a hybrid approach. Regarding manynetwork tasks, e.g., permissions and control, the upper tiers may retainauthority. Contrariwise, when it comes to data administration,authoritative structure may be inverted and the lower tiers may beprioritized.

The computerized network may include a central server. The centralserver may be coupled, directly or indirectly, to a plurality of edgenodes.

The method may include compiling, via a first one of the edge nodes,data associated with a transaction. The method may also include storingthe data, or a relevant portion thereof, as a master dataset in a memoryof the first edge node. Relevant portions may include predeterminedcategories. For example, for a transaction that is a sale, relevantportions may include the transacting parties, date, amount, etc.

The method may further include comparing the master dataset totransactional data stored in the central server. When the transactionaldata in the central server is inconsistent with the master dataset inthe first edge node, the method may include updating the transactionaldata in the central server to be consistent with the master dataset inthe first edge node.

In some embodiments, the method may further include storing, on each oneof the plurality of edge nodes, a copy of the master dataset. The copymay be a full copy. The copy may be a partial copy, and may include onlydata that is predetermined to be relevant. The threshold for relevancymay be different between the first edge-node (i.e., the originator ofthe master dataset) and the rest of the edge-nodes. The copy may, incertain embodiments, include data generated by the other edge-nodes. Forexample, the first edge-node may execute a transaction and generate andstore a master dataset. Other edge-nodes may generate their own versionsof the master dataset based on their own perspectives of the sametransaction via their own sensors.

In certain embodiments, storing the master dataset and the copies may beimplemented as a distributed ledger. In a distributed ledger, multiplecopies of a dataset may be stored separately on multiple computingdevices, in this case the edge-nodes. The distributed ledger may be ablockchain database. The blockchain database may include a plurality ofcoordinated (i.e., synced via consensus) databases. Each coordinateddatabase may be stored on a distinct blockchain node (i.e., a computingdevice that authenticates and stores a copy of the coordinated database)from a plurality of blockchain nodes. Each coordinated database mayinclude linked blocks of hashed data. A block that is linked to aprevious block may include a hashing of the hashed data of the previousblock. In certain embodiments, the data may be encrypted. In otherembodiments, the data may not be hashed or encrypted.

The blockchain may be a public blockchain. A public blockchain may be ablockchain in which anyone can participate. The blockchain may beprivate. A private blockchain may be a blockchain in which writepermissions are exclusive to one entity, while read permissions may bepublic or restricted to certain participants.

In certain preferred embodiments, the blockchain may be semi-private. Asemi-private blockchain may be run by a single entity, and the singleentity may grant access to other qualifying entities. For example, thesingle entity may be a financial institution associated with thetransaction. The other entities may be the edge-devices that are part ofthe network. Storing the copy on each one of the plurality of edge-nodesmay include appending the copy to the distributed ledger on each one ofthe plurality of edge-nodes. Data updates may therefore be enteredand/or authenticated directly by the edge-devices.

The method may, in certain embodiments, further include comparing themaster dataset in the first edge node to the copies in the rest of theplurality of edge nodes. When the comparing determines an inconsistency,the method may include updating the master dataset in the first edgenode and the copies in the rest of the plurality of edge nodes to beconsistent and store the same dataset. The consistent dataset stored byall the nodes may be based on a consensus that is derived from saidcomparing. For example, a consensus may be a majority consensus, and themethod may include updating the data in all the nodes to conform withthe dataset that was stored in the majority of nodes at the time of thecomparison.

In some embodiments, the method may further include deleting thetransactional data in the central server when the transactional data isa duplicate of the master dataset.

In certain embodiments, the method may further include only deleting thetransactional data in the central server when the transactional data isa duplicate of the master dataset, and qualifies to be part of apredetermined dispensable category. A dispensable category may includedata that is not linked to other departments or uses. For example, for atransaction that is an upload of a signed document, the document may beneeded for another transaction, or other suitable use. Such data may notbe dispensable, and the duplicate data in the central server may not bedeleted. By contrast, certain sales may be classified as dispensable,and the method may then include deleting duplicate data.

In some embodiments of the method, the first edge node may be a devicethat executed the transaction. Examples of transactions may include asale, a trade, or a transfer, and/or a submission of documentationassociated with a sale, a trade, or a transfer. The first edge node may,for example, be a point of sale (“POS”) terminal, a phone or computingdevice that executes a transaction, or a scanning device that isconfigured to upload transaction documents.

A bottom-up hierarchical computer network architecture is provided. Thearchitecture may include a central server. The central server mayinclude a memory unit for storing transactional data.

The architecture may also include a plurality of edge nodes. The edgenodes may be coupled to the central server. Each edge node may include aprocessor and a memory unit.

At least a first of the edge nodes may be configured to process atransaction, compile data associated with the transaction, and store thedata as a master dataset in the memory unit of the first edge node.

The architecture may also include a data administration module. The dataadministration module may be configured to compare the master dataset inthe first edge node to the transactional data in the central server.When the transactional data in the central server is inconsistent withthe master dataset in the first edge node, the data administrationmodule may be configured to update the transactional data in the centralserver to be consistent with the master dataset in the first edge node.

In some embodiments of the architecture, the data administration modulemay be further configured to store a copy of the master dataset on eachone of the plurality of edge nodes.

In certain embodiments, one of the plurality of edge nodes may be a partof a distributed ledger database. Storing the copy on each one of theplurality of edge nodes may include appending the copy to thedistributed ledger on each one of the plurality of edge nodes.

In some embodiments of the architecture, the data administration modulemay be further configured to compare the master dataset in the firstedge node to the copies in the rest of the plurality of edge nodes. Whenthe comparison determines an inconsistency, the data administrationmodule may be configured to update the master dataset in the first edgenode and the copies in the rest of the plurality of edge nodes to beconsistent based on a consensus derived from said comparison.

In certain embodiments of the architecture, the data administrationmodule may be further configured to delete the transactional data in thecentral server when the transactional data is a duplicate of the masterdataset.

In some embodiments of the architecture, the data administration modulemay be further configured to only delete the transactional data in thecentral server when the transactional data is a duplicate of the masterdataset, and also qualifies to be part of a predetermined dispensablecategory.

The first edge node may, in certain embodiments, be further configuredto execute the transaction. Exemplary transactions may include a sale, atrade, or a transfer, and/or a submission of documentation associatedwith a sale, a trade, or a transfer. When the first edge node executes atransaction, it may be in a position to process, compile data, and storethe master dataset for the transaction. In other embodiments, the firstedge node may be in a position to process, compile data, and store themaster dataset for a transaction by observing the transaction. Theobservation may be enabled by being physically proximal to, or withinsensor range of, the transaction.

A computer system with bottom-up hierarchical data administration isprovided. The system may include a central server. The central servermay include a memory unit for storing transactional data. The system mayinclude a plurality of edge nodes. The edge nodes may be coupled to thecentral server. Each edge node may include a processor and a memoryunit.

At least a first of the edge nodes may be configured to process atransaction, compile data associated with the transaction, and store thedata as a master dataset in the memory unit of said first edge node.

The system may include a data administration module. The dataadministration module may be configured to compare the master dataset inthe first edge node to the transactional data in the central server.When the transactional data is a duplicate of the master dataset, andthe transactional data qualifies to be part of a predetermineddispensable category, the data administration module may be configuredto delete the transactional data in the central server.

In some embodiments of the system, each one of the plurality of edgenodes may be a part of a distributed ledger database. The dataadministration module may be further configured to store a copy of themaster dataset on each one of the plurality of edge nodes. Storing thecopy on each one of the plurality of edge nodes may include appendingthe copy to the distributed ledger on each one of the plurality of edgenodes.

The data administration module may be further configured to compare themaster dataset in the first edge node to the copies in the rest of theplurality of edge nodes. When the comparison determines aninconsistency, the data administration module may be further configuredto update the master dataset in the first edge node and the copies inthe rest of the plurality of edge nodes to be consistent based on aconsensus derived from the comparison.

In some embodiments of the system, the data administration module may befurther configured to update the transactional data in the centralserver to be consistent with the master dataset in the first edge node.The update may be executed when the comparison determines that thetransactional data in the central server is inconsistent with the masterdataset in the first edge node.

Apparatus and methods described herein are illustrative. Apparatus andmethods in accordance with this disclosure will now be described inconnection with the figures, which form a part hereof. The figures showillustrative features of apparatus and method steps in accordance withthe principles of this disclosure. It is understood that otherembodiments may be utilized, and that structural, functional, andprocedural modifications may be made without departing from the scopeand spirit of the present disclosure.

FIG. 1 shows illustrative network architecture 100 according to aspectsof the disclosure. Architecture 100 may include a central server 101.Central server 101 may be coupled with data depository 103(alternatively referred to as a data repository). Central server 101 maybe coupled with processing engine 105. Processing engine 105 may beoperable with machine-learning (“ML”) model 107.

Some or all of architecture elements 101-107 may be physically locatedin a central location. Some or all of architecture elements 101-107 maybe logically centralized. Some or all of architecture elements 101-107may be cloud-based.

Architecture 100 may include edge-nodes 109-115. Edge-nodes 109-115 maybe devices at the network edge—i.e., devices at or near the interfacewith the environment outside of the network. Edge-nodes 109-115 may bedevices that are capable of generating, processing, and storing data atthe edge, without relying on central server 101. Each of edge-nodes109-115 may include a sensor, a processor, and a memory.

FIG. 2 shows illustrative network architecture 200 according to aspectsof the disclosure. Architecture 200 may include a central server 201. Insome embodiments, architecture 200 may be a distributed network and maynot include distinct central components.

Architecture 200 may include edge-nodes 203-221. Edge-nodes 203-221 mayinclude a video camera, light bulb, smart watch, smart glasses, computer(e.g., laptop, desktop, tablet), smart thermostat, shoe, car, shirt, andsmartphone, respectively. Each of edge-nodes 203-221 may include atleast a sensor, a processor, and a memory. Each of edge-nodes 203-221may be configured to communicate with other devices. Some of the nodesmay communicate with the central server directly. Others may communicatewith intermediate devices (intermediate devices may be referred toalternatively as edge servers) that may or may not then communicate withthe central server. Architecture 200 shows connecting lines betweenedge-nodes 203-221 to show illustrative connective routes. For example,architecture 200 shows that while car 217 and computer 211 may beconfigured to communicate directly with central server 201, video camera203 and light bulb 205 may be configured to communicate with the networkvia computer 211.

FIG. 3 shows illustrative bottom-up hierarchical architecture 300according to aspects of the disclosure. Architecture 300 may includecentral server 301. Central server 301 may be depicted at the top tierof the hierarchy. In many respects, central server 301 may haveauthoritative status over the devices in the lower tiers. Lower tiersmay include edge-server 303 and edge-server 305 in an intermediate tier,and edge-nodes 307-313 in a bottom tier. However, regarding dataadministration, authority may be reversed, and the lower tiers may havegreater authority than the higher tiers.

FIG. 4 shows illustrative flowchart 400 of a logical flow according toaspects of the disclosure. Flowchart 400 may represent one exemplaryembodiment—other embodiments may include different logical steps and/orstep sequences than those shown in flowchart 400.

Flowchart 400 may begin with executing a transaction at step 401. Thetransaction may, for example, be a sale executed by an edge-device, suchas a POS terminal. The edge device may compile transaction data at step403, and store the data as a master dataset at step 405. If the data isnot determined to be dispensable at step 407, i.e., the data may beneeded by other elements of the network, the flowchart proceeds to step413. If the data is determined to be dispensable at step 407, i.e., thedata should not be needed by other elements of the network, theflowchart proceeds to step 409, where the system checks for duplicatesin the central server. If no duplicate is found, the flowchart proceedsto step 413. If a duplicate is found, the duplicate may be deleted fromthe transactional data stored in the central server at step 411.

Step 413 may include storing copies of the master dataset on multipleedge-nodes. Storing the copies may be implemented as a distributedledger. The copies may be received from the edge-device that executedthe transaction. Alternatively, the copies may be generatedindependently by the other edge-devices, e.g., based on their sensorperspective of the transaction.

Step 415 may include comparing the data across the nodes. If aninconsistency is determined at step 417, the datasets may be updatedbased on a consensus derived from the comparing.

Thus, systems and methods are provided for a bottom-up hierarchicalarchitecture for data administration in a digital network. Advantages ofthe unique architecture include providing consensus-backedauthentication for the data located at the network edge, as well asgiving the edge-data higher authority over the data at the typicallyauthoritative central network.

The steps of methods may be performed in an order other than the ordershown and/or described herein. Embodiments may omit steps shown and/ordescribed in connection with illustrative methods. Embodiments mayinclude steps that are neither shown nor described in connection withillustrative methods.

Illustrative method steps may be combined. For example, an illustrativemethod may include steps shown in connection with another illustrativemethod.

Apparatus may omit features shown and/or described in connection withillustrative apparatus. Embodiments may include features that areneither shown nor described in connection with the illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative embodiment may include features shown inconnection with another illustrative embodiment.

The drawings show illustrative features of apparatus and methods inaccordance with the principles of the invention. The features areillustrated in the context of selected embodiments. It will beunderstood that features shown in connection with one of the embodimentsmay be practiced in accordance with the principles of the inventionalong with features shown in connection with another of the embodiments.

One of ordinary skill in the art will appreciate that the steps shownand described herein may be performed in other than the recited orderand that one or more steps illustrated may be optional. The methods ofthe above-referenced embodiments may involve the use of any suitableelements, steps, computer-executable instructions, or computer-readabledata structures. In this regard, other embodiments are disclosed hereinas well that can be partially or wholly implemented on acomputer-readable medium, for example, by storing computer-executableinstructions or modules or by utilizing computer-readable datastructures.

Thus, methods and systems for edge-computing-based bottom-uphierarchical architecture for data administration in a digital networkare provided. Persons skilled in the art will appreciate that thepresent invention can be practiced by other than the describedembodiments, which are presented for purposes of illustration ratherthan of limitation, and that the present invention is limited only bythe claims that follow.

What is claimed is:
 1. A method for bottom-up hierarchical dataadministration in a computerized network, said computerized networkcomprising a central server coupled to a plurality of edge nodes, themethod comprising: compiling, via a first one of the edge nodes, dataassociated with a transaction; storing the data as a master dataset in amemory of the first edge node; comparing the master dataset totransactional data stored in the central server; and when thetransactional data in the central server is inconsistent with the masterdataset in the first edge node, updating the transactional data in thecentral server to be consistent with the master dataset in the firstedge node.
 2. The method of claim 1, further comprising storing a copyof the master dataset on each one of the plurality of edge nodes.
 3. Themethod of claim 2, wherein each one of the plurality of edge nodes is apart of a distributed ledger database, and the storing the copy on eachone of the plurality of edge nodes comprises appending the copy to thedistributed ledger on each one of the plurality of edge nodes.
 4. Themethod of claim 2, further comprising: comparing the master dataset inthe first edge node to the copies in the rest of the plurality of edgenodes; and when the comparing determines an inconsistency, updating themaster dataset in the first edge node and the copies in the rest of theplurality of edge nodes to be consistent based on a consensus derivedfrom said comparing.
 5. The method of claim 1, further comprisingdeleting the transactional data in the central server when thetransactional data is a duplicate of the master dataset.
 6. The methodof claim 5, further comprising only deleting the transactional data inthe central server when the transactional data: is a duplicate of themaster dataset; and qualifies to be part of a predetermined dispensablecategory.
 7. The method of claim 1, wherein the first edge node is apoint of sale (“POS”) terminal that executed the transaction.
 8. Themethod of claim 1, wherein the transaction is a sale, a trade, or atransfer, and/or a submission of documentation associated with a sale, atrade, or a transfer.
 9. A bottom-up hierarchical computer networkarchitecture, said architecture comprising: a central server, saidcentral server comprising a memory unit for storing transactional data;a plurality of edge nodes coupled to the central server, each edge nodecomprising a processor and a memory unit, wherein at least a first ofthe edge nodes is configured to: process a transaction; compile dataassociated with the transaction; and store the data as a master datasetin the memory unit of said first edge node; and a data administrationmodule, said data administration module configured to: compare themaster dataset in the first edge node to the transactional data in thecentral server; and when the transactional data in the central server isinconsistent with the master dataset in the first edge node, update thetransactional data in the central server to be consistent with themaster dataset in the first edge node.
 10. The architecture of claim 9,wherein the data administration module is further configured to store acopy of the master dataset on each one of the plurality of edge nodes.11. The architecture of claim 10, wherein each one of the plurality ofedge nodes is a part of a distributed ledger database, and storing thecopy on each one of the plurality of edge nodes comprises appending thecopy to the distributed ledger on each one of the plurality of edgenodes.
 12. The architecture of claim 10, wherein the data administrationmodule is further configured to: compare the master dataset in the firstedge node to the copies in the rest of the plurality of edge nodes; andwhen the comparison determines an inconsistency, update the masterdataset in the first edge node and the copies in the rest of theplurality of edge nodes to be consistent based on a consensus derivedfrom said comparison.
 13. The architecture of claim 9, wherein the dataadministration module is further configured to delete the transactionaldata in the central server when the transactional data is a duplicate ofthe master dataset.
 14. The architecture of claim 13, wherein the dataadministration module is further configured to only delete thetransactional data in the central server when the transactional data: isa duplicate of the master dataset; and qualifies to be part of apredetermined dispensable category.
 15. The architecture of claim 9,wherein the first edge node is further configured to execute thetransaction.
 16. The architecture of claim 9, wherein the transaction isa sale, a trade, or a transfer, and/or a submission of documentationassociated with a sale, a trade, or a transfer.
 17. A computer systemwith bottom-up hierarchical data administration, said system comprising:a central server, said central server comprising a memory unit forstoring transactional data; a plurality of edge nodes coupled to thecentral server, each edge node comprising a processor and a memory unit,wherein at least a first of the edge nodes is configured to: process atransaction; compile data associated with the transaction; and store thedata as a master dataset in the memory unit of said first edge node; anda data administration module, said data administration module configuredto: compare the master dataset in the first edge node to thetransactional data in the central server; and when the transactionaldata is a duplicate of the master dataset, and the transactional dataqualifies to be part of a predetermined dispensable category, delete thetransactional data in the central server.
 18. The system of claim 17,wherein: each one of the plurality of edge nodes is a part of adistributed ledger database; and the data administration module isfurther configured to: store a copy of the master dataset on each one ofthe plurality of edge nodes, wherein said storing the copy on each oneof the plurality of edge nodes comprises appending the copy to thedistributed ledger on each one of the plurality of edge nodes; comparethe master dataset in the first edge node to the copies in the rest ofthe plurality of edge nodes; and when the comparison determines aninconsistency, update the master dataset in the first edge node and thecopies in the rest of the plurality of edge nodes to be consistent basedon a consensus derived from said comparison.
 19. The system of claim 17,wherein the data administration module is further configured to updatethe transactional data in the central server to be consistent with themaster dataset in the first edge node when the comparison determinesthat the transactional data in the central server is inconsistent withthe master dataset in the first edge node.
 20. The system of claim 17,wherein the first edge node is further configured to execute thetransaction, and the transaction is a sale, a trade, or a transfer,and/or a submission of documentation associated with a sale, a trade, ora transfer.